Classical ct reconstruction algorithms include fbp [1]}, [2]}, which has been superseded by more robust iterative algebraic reconstruction techniques [3]}, [4]} in clinical practice.
In light of dose reduction, these algorithms may be equipped with prior knowledge to increase reconstruction quality of low-dose scans.
Traditional, hand-crafted regularizers, such as tv [5]} and extensions such as tgv [6]}, typically encode regularity assumptions of the reconstruction, such as sparsity of gradients.
These hand-crafted regularizers have been used extensively and successfully in reconstruction problems [7]}, [8]}, [9]}, however they do not fully model the a-priori available information.
To capture also higher-order image statistics, the idea of learning a regularizer from data emerged [10]}, [11]}, [12]}.
Although these learning-based approaches are now dominant in many fields, such models have classically focused on modeling local statistics and leave much to be desired in modeling global dependencies.
